Training and Research

PhD school courses/classes - 2023/2024

Please note: Additional information will be added during the year. Currently missing information is labelled as “TBD” (i.e. To Be Determined).

PhD students must obtain a specified number of CFUs each year by attending teaching activities offered by the PhD School.
First and second year students must obtain 8 CFUs. Teaching activities ex DM 226/2021 provide 5 CFUs; free choice activities provide 3 CFUs.
Third year students must obtain 4 CFUs. Teaching activities ex DM 226/2021 provide 2 CFUs; free choice activities provide 2 CFUs.

Registering for the courses is not required unless explicitly indicated; please consult the course information to verify whether registration is required or not. When registration is actually required, no confirmation e-mail will be sent after signing up.

Teaching Activities ex DM 226/2021: Linguistic Activities

Teaching Activities ex DM 226/2021: Research management and Enhancement

Teaching Activities ex DM 226/2021: Statistics and Computer Sciences

Teaching Activities: Free choice

Credits

2

Language

English

Class attendance

Free Choice

Location

VERONA

Learning objectives

An introduction to the fundamentals of generalized regression models will be given in this course, with a focus on models for count, binary, and categorical data. These types of response variables are widely used in industrial applications as well as observational and experimental research.
Upon successful completion of the course, students will be able to:
• Describe the general structure of a GLM and similarities and differences with linear models
• Estimate and interpret a logistic regression model
• Estimate and interpret a Poisson regression model
• Know of issues and some strategies for dealing with overdispersion in some generalised linear models (GLMs)

Prerequisites and basic notions

This course assumes a good understanding of probability and mid-level knowledge of linear regression theory.

Program

The course covers methods for regression analysis of responses that do not follow the normal distribution, especially of discrete responses. We will learn to understand some of the common statistical methods for fitting regression models to such data. In particular, we will consider logistic regression, Poisson regression and log-linear models. The lecture focuses on the development, theoretical justification, and interpretation of these methods.

When and where

Teaching forms mainly consist of lectures (8h) and exercises proposed by the teacher. The teaching material (slides of the theoretical lessons) is made available to the students on the e-learning web page of the course (Moodle platform). Lessons will be delivered via Zoom. Full attendance is required.

Learning assessment procedures

There is no exam

Students with disabilities or specific learning disorders (SLD), who intend to request the adaptation of the exam, must follow the instructions given HERE

Assessment

There is no exam, hence there is also no definition of the evaluation criteria.

Criteria for the composition of the final grade

There is no grade because there is no exam.

Scheduled Lessons

When Classroom Teacher topics
Tuesday 19 March 2024
14:30 - 16:30
Duration: 2:00 AM
To be defined Lucia Cazzoletti Introduction to generalised linear models. Review of the general linear model: assumptions of the linear model (independence of observations, homoskedasticity of errors, linearity of coeffiicents) , least squares estimation and maximum likelihood estimation. Main features of the genralised linear model: i) the probability distribution function of the random component of the response variable belongs to the exponential family, ii) a differentiable and monotonic link function relates the mean of the response variable to the linear predictor, a linear combination of coefficients and explanatory variables.
Tuesday 26 March 2024
14:30 - 16:30
Duration: 2:00 AM
To be defined Lucia Cazzoletti Introduction to the theoretical basis of logistic regression model, commonly used for binary (proportion/percentage) data, as a generalised linear model. Binomial distribution for the outcome binary variable. The link between probability and logodds. Maximum likelihood estimation of the coefficients of the model. Interpretation of the meaning of the regression coefficients and their statistical significance.
Wednesday 03 April 2024
14:30 - 16:30
Duration: 2:00 AM
To be defined Lucia Cazzoletti Introduction to the theoretical basis of Poisson regression model, commonly used for count data, as a generalised linear model. Poisson distribution for the outcome variable. Maximum likelihood estimation of the coefficients of the model. Interpretation of the meaning of the regression coefficients and their statistical significance. Use of the offset to take into account the different exposure of subjects. Extensions of the Poisson Regression Model: Negative binomial regression model (NBRM), Zero-inflated poisson (ZIP) model, Zero-truncated count data model.
Wednesday 24 April 2024
14:30 - 16:30
Duration: 2:00 AM
Aula virtuale - Lezione online Lucia Cazzoletti Using Deviances to Compare Models for Logistic and for Poisson Regression Models. Use of the Likelihood Ratio Test to assess the presence of overdispersion. Some hints about the log-linear model in the presence of contingency tables

Faculty

A B C D F G L M O P R S T Z

Antoniazzi Franco

symbol email franco.antoniazzi@univr.it symbol phone-number +39 045 812 7811-7131

Bencivenga Maria

symbol email maria.bencivenga@univr.it symbol phone-number +39 045 812 7053

Conci Simone

symbol email simone.conci@univr.it

De Santis Daniele

symbol email daniele.desantis@univr.it symbol phone-number +39 045 812 4251 - 4097

Ferrante Giuliana

symbol email giuliana.ferrante@univr.it symbol phone-number 045/8127858

Gaudino Rossella

symbol email rossella.gaudino@univr.it symbol phone-number +39 045 812 7121

Gottin Leonardo

symbol email leonardo.gottin@univr.it symbol phone-number +39 045 812 7250

Lombardo Giorgio

symbol email giorgio.lombardo@univr.it symbol phone-number +39 045 812 4867

Luciani Giovanni Battista

symbol email giovanni.luciani@univr.it symbol phone-number +39 045 812 3337

Maffeis Claudio

symbol email claudio.maffeis@univr.it symbol phone-number +39 045 812 7664

Onorati Francesco

symbol email francesco.onorati@univr.it symbol phone-number 045/8121945

Pedrazzani Corrado

symbol email corrado.pedrazzani@univr.it symbol phone-number +39 045 812 6719

Pietrobelli Angelo

symbol email angelo.pietrobelli@univr.it symbol phone-number +39 045 812 7125

Ribichini Flavio Luciano

symbol email flavio.ribichini@univr.it symbol phone-number +39 045 812 2039

Rungatscher Alessio

symbol email alessio.rungatscher@univr.it symbol phone-number +39 045 812 3307

Ruzzenente Andrea

symbol email andrea.ruzzenente@univr.it symbol phone-number +39 045 812 4464

Salvia Roberto

symbol email roberto.salvia@univr.it symbol phone-number +39 045 812 4816

Schweiger Vittorio

symbol email vittorio.schweiger@univr.it symbol phone-number +39 045 812 4311

Trevisiol Lorenzo

symbol email lorenzo.trevisiol@univr.it symbol phone-number +39 045 812 4023

Zaffanello Marco

symbol email marco.zaffanello@univr.it symbol phone-number +39 045 812 7126

Zotti Francesca

symbol email francesca.zotti@univr.it symbol phone-number +39 045 812 6938

PhD students

PhD students present in the:

No people are present. 40° Ciclo not started.

Course lessons
PhD Schools lessons

Loading...

Guidelines for PhD students

Below you will find the files that contain the Guidelines for PhD students and rules for the acquisition of ECTS credits (in Italian: "CFU") for the Academic Year 2023/2024.